《计算机应用》唯一官方网站 ›› 2022, Vol. 42 ›› Issue (7): 2192-2200.DOI: 10.11772/j.issn.1001-9081.2021060900
• 多媒体计算与计算机仿真 • 上一篇
收稿日期:
2021-06-03
修回日期:
2021-08-01
接受日期:
2021-08-06
发布日期:
2021-08-01
出版日期:
2022-07-10
通讯作者:
胡晓
作者简介:
谭湘粤(1998—),女,湖南衡阳人,硕士研究生,主要研究方向:深度学习、伪装目标检测基金资助:
Xiangyue TAN1, Xiao HU2(), Jiaxin YANG1, Junjiang XIANG1
Received:
2021-06-03
Revised:
2021-08-01
Accepted:
2021-08-06
Online:
2021-08-01
Published:
2022-07-10
Contact:
Xiao HU
About author:
TAN Xiangyue, born in 1998, M. S. candidate. Her research interests include deep learning, camouflaged object detection.Supported by:
摘要:
伪装目标检测(COD)旨在检测隐藏在复杂环境中的目标。现有COD算法在结合多层次特征时,忽略了特征的表达和融合方式对检测性能的影响。为此,提出一种基于递进式特征增强聚合的COD算法。首先,通过主干网络提取多级特征;然后,为了提高特征的表达能力,使用由特征增强模块(FEM)构成的增强网络对多层次特征进行增强;最后,在聚合网络中设计邻近聚合模块(AAM)实现相邻特征之间的信息融合,以突显伪装目标区域的特征,并提出新的递进式聚合策略(PAS)通过渐进的方式聚合邻近特征,从而在实现多层特征有效融合的同时抑制噪声。在3个公开数据集上的实验表明,所提算法相较于12种最先进的算法在4个客观评价指标上均取得最优表现,尤其是在COD10K数据集上所提算法的加权的F测评法和平均绝对误差(MAE)分别达到了0.809和0.037。由此可见,所提算法在COD任务上拥有较优的性能。
中图分类号:
谭湘粤, 胡晓, 杨佳信, 向俊将. 基于递进式特征增强聚合的伪装目标检测[J]. 计算机应用, 2022, 42(7): 2192-2200.
Xiangyue TAN, Xiao HU, Jiaxin YANG, Junjiang XIANG. Camouflaged object detection based on progressive feature enhancement aggregation[J]. Journal of Computer Applications, 2022, 42(7): 2192-2200.
方法 | CHAMELEON | CAMO-Test | ||||||
---|---|---|---|---|---|---|---|---|
SA+CA+RF | 0.889 | 0.938 | 0.819 | 0.032 | 0.799 | 0.861 | 0.712 | 0.079 |
SA+RF+CA | 0.890 | 0.945 | 0.816 | 0.030 | 0.798 | 0.859 | 0.715 | 0.078 |
RF+SA+CA | 0.891 | 0.947 | 0.822 | 0.031 | 0.805 | 0.862 | 0.720 | 0.075 |
RF+CA+SA | 0.886 | 0.940 | 0.811 | 0.031 | 0.800 | 0.858 | 0.714 | 0.077 |
CA+RF+SA | 0.894 | 0.944 | 0.825 | 0.029 | 0.803 | 0.863 | 0.717 | 0.075 |
CA+SA+RF | 0.895 | 0.948 | 0.826 | 0.028 | 0.808 | 0.867 | 0.726 | 0.075 |
表1 CHAMELEON和CAMO数据集上FEM的消融实验结果
Tab.1 Ablation experimental results of FEM on CHAMELEON and CAMO datasets
方法 | CHAMELEON | CAMO-Test | ||||||
---|---|---|---|---|---|---|---|---|
SA+CA+RF | 0.889 | 0.938 | 0.819 | 0.032 | 0.799 | 0.861 | 0.712 | 0.079 |
SA+RF+CA | 0.890 | 0.945 | 0.816 | 0.030 | 0.798 | 0.859 | 0.715 | 0.078 |
RF+SA+CA | 0.891 | 0.947 | 0.822 | 0.031 | 0.805 | 0.862 | 0.720 | 0.075 |
RF+CA+SA | 0.886 | 0.940 | 0.811 | 0.031 | 0.800 | 0.858 | 0.714 | 0.077 |
CA+RF+SA | 0.894 | 0.944 | 0.825 | 0.029 | 0.803 | 0.863 | 0.717 | 0.075 |
CA+SA+RF | 0.895 | 0.948 | 0.826 | 0.028 | 0.808 | 0.867 | 0.726 | 0.075 |
方法 | CHAMELEON | CAMO-Test | ||||||
---|---|---|---|---|---|---|---|---|
三邻近-二邻近 | 0.885 | 0.939 | 0.813 | 0.033 | 0.801 | 0.858 | 0.715 | 0.077 |
二邻近-三邻近 | 0.884 | 0.936 | 0.813 | 0.033 | 0.805 | 0.862 | 0.717 | 0.076 |
二邻近 | 0.895 | 0.948 | 0.826 | 0.028 | 0.808 | 0.867 | 0.726 | 0.075 |
表2 CHAMELEON和CAMO数据集上AAM的消融实验结果
Tab.2 Ablation experimental results of AAM on CHAMELEON and CAMO datasets
方法 | CHAMELEON | CAMO-Test | ||||||
---|---|---|---|---|---|---|---|---|
三邻近-二邻近 | 0.885 | 0.939 | 0.813 | 0.033 | 0.801 | 0.858 | 0.715 | 0.077 |
二邻近-三邻近 | 0.884 | 0.936 | 0.813 | 0.033 | 0.805 | 0.862 | 0.717 | 0.076 |
二邻近 | 0.895 | 0.948 | 0.826 | 0.028 | 0.808 | 0.867 | 0.726 | 0.075 |
方法 | COD10K-Test | |||
---|---|---|---|---|
No.1 | 0.794 | 0.869 | 0.637 | 0.041 |
No.2 | 0.797 | 0.874 | 0.644 | 0.040 |
No.3 | 0.805 | 0.881 | 0.667 | 0.038 |
No.4 | 0.809 | 0.886 | 0.673 | 0.037 |
表3 COD10K数据集上网络间的消融实验结果
Tab. 3 Ablation experimental results between networks on COD10K dataset
方法 | COD10K-Test | |||
---|---|---|---|---|
No.1 | 0.794 | 0.869 | 0.637 | 0.041 |
No.2 | 0.797 | 0.874 | 0.644 | 0.040 |
No.3 | 0.805 | 0.881 | 0.667 | 0.038 |
No.4 | 0.809 | 0.886 | 0.673 | 0.037 |
算法 | CHAMELEON | CAMO-Test | COD10K-Test | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
FPN | 0.794 | 0.783 | 0.590 | 0.075 | 0.684 | 0.677 | 0.483 | 0.131 | 0.697 | 0.691 | 0.411 | 0.075 |
UNet++ | 0.695 | 0.762 | 0.501 | 0.094 | 0.599 | 0.653 | 0.392 | 0.149 | 0.623 | 0.672 | 0.350 | 0.086 |
ANet-SRM | — | — | — | — | 0.682 | 0.685 | 0.484 | 0.126 | — | — | — | — |
PFANet | 0.679 | 0.648 | 0.378 | 0.144 | 0.659 | 0.622 | 0.391 | 0.172 | 0.636 | 0.618 | 0.286 | 0.128 |
PoolNet | 0.776 | 0.779 | 0.555 | 0.081 | 0.702 | 0.698 | 0.494 | 0.129 | 0.705 | 0.713 | 0.416 | 0.074 |
CPD | 0.853 | 0.866 | 0.706 | 0.052 | 0.726 | 0.729 | 0.550 | 0.115 | 0.747 | 0.770 | 0.508 | 0.059 |
EGNet | 0.848 | 0.870 | 0.702 | 0.050 | 0.732 | 0.768 | 0.583 | 0.104 | 0.737 | 0.779 | 0.509 | 0.056 |
SINet | 0.869 | 0.891 | 0.740 | 0.044 | 0.751 | 0.771 | 0.606 | 0.100 | 0.771 | 0.806 | 0.551 | 0.051 |
PraNet | 0.860 | 0.907 | 0.763 | 0.044 | 0.769 | 0.824 | 0.663 | 0.094 | 0.789 | 0.861 | 0.629 | 0.045 |
MCIF-Net | — | — | — | — | 0.784 | 0.845 | 0.677 | 0.084 | 0.787 | 0.872 | 0.636 | 0.042 |
Rank-Net | 0.893 | 0.938 | — | 0.033 | 0.793 | 0.826 | — | 0.085 | 0.793 | 0.868 | — | 0.041 |
TANet | 0.888 | 0.911 | 0.786 | 0.036 | 0.793 | 0.834 | 0.690 | 0.083 | 0.803 | 0.848 | 0.629 | 0.041 |
本文算法 | 0.895 | 0.948 | 0.826 | 0.028 | 0.808 | 0.867 | 0.726 | 0.075 | 0.809 | 0.886 | 0.673 | 0.037 |
表4 各算法在3个COD数据集上的客观评价
Tab.4 Objective evaluation of different algorithms on three COD datasets
算法 | CHAMELEON | CAMO-Test | COD10K-Test | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
FPN | 0.794 | 0.783 | 0.590 | 0.075 | 0.684 | 0.677 | 0.483 | 0.131 | 0.697 | 0.691 | 0.411 | 0.075 |
UNet++ | 0.695 | 0.762 | 0.501 | 0.094 | 0.599 | 0.653 | 0.392 | 0.149 | 0.623 | 0.672 | 0.350 | 0.086 |
ANet-SRM | — | — | — | — | 0.682 | 0.685 | 0.484 | 0.126 | — | — | — | — |
PFANet | 0.679 | 0.648 | 0.378 | 0.144 | 0.659 | 0.622 | 0.391 | 0.172 | 0.636 | 0.618 | 0.286 | 0.128 |
PoolNet | 0.776 | 0.779 | 0.555 | 0.081 | 0.702 | 0.698 | 0.494 | 0.129 | 0.705 | 0.713 | 0.416 | 0.074 |
CPD | 0.853 | 0.866 | 0.706 | 0.052 | 0.726 | 0.729 | 0.550 | 0.115 | 0.747 | 0.770 | 0.508 | 0.059 |
EGNet | 0.848 | 0.870 | 0.702 | 0.050 | 0.732 | 0.768 | 0.583 | 0.104 | 0.737 | 0.779 | 0.509 | 0.056 |
SINet | 0.869 | 0.891 | 0.740 | 0.044 | 0.751 | 0.771 | 0.606 | 0.100 | 0.771 | 0.806 | 0.551 | 0.051 |
PraNet | 0.860 | 0.907 | 0.763 | 0.044 | 0.769 | 0.824 | 0.663 | 0.094 | 0.789 | 0.861 | 0.629 | 0.045 |
MCIF-Net | — | — | — | — | 0.784 | 0.845 | 0.677 | 0.084 | 0.787 | 0.872 | 0.636 | 0.042 |
Rank-Net | 0.893 | 0.938 | — | 0.033 | 0.793 | 0.826 | — | 0.085 | 0.793 | 0.868 | — | 0.041 |
TANet | 0.888 | 0.911 | 0.786 | 0.036 | 0.793 | 0.834 | 0.690 | 0.083 | 0.803 | 0.848 | 0.629 | 0.041 |
本文算法 | 0.895 | 0.948 | 0.826 | 0.028 | 0.808 | 0.867 | 0.726 | 0.075 | 0.809 | 0.886 | 0.673 | 0.037 |
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